The complexity of CT image reconstruction requires tens to hundreds of billions of computations per second. Until few years ago, special purpose processors designed especially for such applications were used. Such processors require significant design effort and are thus difficult to change as media-processing applications and algorithms evolve and have limited parallelism. The demand for flexibility in medical applications motivated the use of stream processors with massively parallel architecture.

Stream processing architectures offers data parallel kind of parallelism. In data parallelism, a set of operations are performed on large amount of data in a parallel fashion. Many of the reconstruction algorithms are high suitable for this data parallel paradigm. The central idea behind stream processing is to organize the application into streams – set of data – and kernels – a series of operations applied to each element in the stream.